mcpField
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tooltrace

tooltrace

Remote
tooltrace.ioBrowser Automation0Updated 7w ago

About tooltrace

ToolTrace is an MCP server that gives AI agents and MCP clients tools for extracting web content and inspecting sites. It supports hosted HTTP connections or local stdio, with browser rendering available when needed.

Key features

  1. 01Extract page content, metadata, and links
  2. 02Parse JSON-LD structured data
  3. 03Run on-page SEO audits
  4. 04Detect website technology stacks
  5. 05Inspect XML sitemaps
  6. 06Support static fetch and browser rendering

Use cases

  1. 01Turn webpage content into Markdown
  2. 02Review a site's SEO and tech stack
  3. 03Extract structured data from product pages
  4. 04Inspect a website sitemap

MCP support

The server exposes tools for web scraping, metadata and link extraction, SEO audits, tech-stack detection, and sitemap inspection. Clients can connect through a hosted HTTP endpoint or local stdio.

Configuration

Remote servers need no local install. Pick your client, then copy the config or install in one click.

Endpointhttps://mcp.tooltrace.io/mcp
Copy install command
Claude CodeTerminal
Cursor.cursor/mcp.json
VS Code.vscode/mcp.json
Claude Desktopclaude_desktop_config.json
Windsurf~/.codeium/windsurf/mcp_config.json

Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.

All endpoints
Streamable HTTPhttps://mcp.tooltrace.io/mcpAuth required
Packages
npm@tooltrace/mcp-server0.1.2

Tools

7
ParameterTypeRequiredDescription
urlstringYesPublic webpage URL to extract
modeenumNo'structured' for parsed data, 'raw' for HTML, 'both' for everything.structured | raw | both
renderenumNoRendering mode. 'never' = fast static fetch (1 credit). 'auto' = static first, browser if needed. 'always' = browser rendering (5 credits).never | auto | always
includearrayNoWhich fields to include in the response.
wait_untilenumNoBrowser navigation milestone. Only used with browser rendering.domcontentloaded | load | networkidle
wait_for_selectorstringNoCSS selector to wait for on rendered pages.

Details

From the project README

ToolTrace MCP Server

Give AI agents a complete web intelligence toolkit. This Model Context Protocol server connects Claude Code, Claude Desktop, Cursor, VS Code, Codex, Windsurf and any other MCP client to the ToolTrace API.

One tool per ToolTrace endpoint: scrape any page to clean Markdown, read metadata and JSON-LD, extract links, run on-page SEO audits, detect a site's tech stack, and check XML sitemaps. Free tier included, no card required.

Setup guide: tooltrace.io/mcp

Tools

ToolDescription
tooltrace_extractExtract clean Markdown, text, metadata, links, JSON-LD, and sections from any webpage
tooltrace_metadataGet page title, description, canonical URL, Open Graph, and Twitter card fields
tooltrace_linksExtract all internal and external links with anchor text
tooltrace_schemaExtract JSON-LD structured data (Article, Product, FAQ, etc.)
tooltrace_seo_auditRun an SEO audit with scored checks and recommendations
tooltrace_tech_stackDetect CMS, frameworks, analytics, CDN, hosting, and more
tooltrace_sitemapInspect and parse XML sitemaps

Two ways to connect

Hosted, for clients that take a connector URL. Nothing to install or update:

https://mcp.tooltrace.io/mcp
claude mcp add --transport http tooltrace https://mcp.tooltrace.io/mcp \
  --header "Authorization: Bearer your-api-key"

Also listed on Smithery.

Local, over stdio, for clients that run a process. Use this when your client does not speak HTTP, or when you would rather your API key never left your own machine. That is the quick start below.

Both expose the same tools and bill the same credits.

Quick start

1. Get a free API key

Sign up at tooltrace.io/signup. The free plan includes 1,000 credits per month.

2. Install
npm install -g @tooltrace/mcp-server
3. Configure your MCP client

Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "tooltrace": {
      "command": "tooltrace-mcp",
      "env": {
        "TOOLTRACE_API_KEY": "your-api-key"
      }
    }
  }
}

Claude Code:

claude mcp add tooltrace tooltrace-mcp -e TOOLTRACE_API_KEY=your-api-key

Cursor (.cursor/mcp.json):

{
  "mcpServers": {
    "tooltrace": {
      "command": "tooltrace-mcp",
      "env": {
        "TOOLTRACE_API_KEY": "your-api-key"
      }
    }
  }
}

VS Code (.vscode/mcp.json):

{
  "servers": {
    "tooltrace": {
      "command": "tooltrace-mcp",
      "env": {
        "TOOLTRACE_API_KEY": "your-api-key"
      }
    }
  }
}

Codex (~/.codex/config.toml):

[mcp_servers.tooltrace]
command = "tooltrace-mcp"
env = { TOOLTRACE_API_KEY = "your-api-key" }

Windsurf (mcp_config.json):

{
  "mcpServers": {
    "tooltrace": {
      "command": "tooltrace-mcp",
      "env": {
        "TOOLTRACE_API_KEY": "your-api-key"
      }
    }
  }
}

Any other MCP client: run tooltrace-mcp over stdio and set the TOOLTRACE_API_KEY environment variable. Full per-client setup guide at tooltrace.io/mcp.

Run with npx (no install)
TOOLTRACE_API_KEY=your-api-key npx @tooltrace/mcp-server

Usage examples

Once connected, your AI agent can:

  • "Extract the main content from this blog post as Markdown"
  • "What technologies does competitor.com use?"
  • "Run an SEO audit on our landing page"
  • "Get all the links from this documentation page"
  • "Extract the JSON-LD schema from this product page"
  • "Check the sitemap for this website"

Rendering modes

All tools support a render parameter:

  • never: Fast static fetch, 1 credit. Best for server-rendered pages.
  • auto (default): Starts static, renders in browser if needed. 1 or 5 credits.
  • always: Full browser rendering, 5 credits. For JavaScript-heavy SPAs.

Development

git clone https://github.com/ToolTrace-io/mcp-server.git
cd mcp-server
npm install
npm run build
TOOLTRACE_API_KEY=your-key node dist/index.js

License

MIT

Comments

Markdown supported

New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.